To diagnose Alzheimer's disease etc., measuring and imaging functional status such as bloodstream or glucose metabolism of each point of patient's brain is carried out. In positron emission tomography (hereinafter referred as “PET”), medical agent which is indicated by positron emission nuclear such as 18F-FDG is injected to obtain the functional status such as glucose metabolism by measuring gamma ray amount as annihilation radiation at each point of patient's brain. In single photon emission computed tomography (hereinafter referred as “SPECT”), gamma ray emission nuclear species such as 123I and 99mTc is used for the same purpose. As shown in FIG. 1, plural sectional images are generated by measuring gamma ray amount of each section of patient's brain. In the sectional image, for example, red, yellow, green and blue are used for showing areas in descending order of status value such as bloodstream or glucose metabolism associated value (voxel value associating functional value measured by PET of SPECT).
Diagnosis of disease can be carried out by comparing data showing status value of normal healthy subject and data showing status value of patient. For comparing, computer displays differential image between the sectional image of normal healthy subject and the sectional image of patient. To obtain the differential image, the sectional image of patient is spatially fitted to that of normal healthy subject and Z-score at each point is calculated. The method to achieve such diagnosis is well known such as 3-Dimentional stereotaxic surface projection (3D-SSP) developed by Minoshima of Washington University, and Statistical Parametoric Mapping (SPM) developed by Friston et al. of Hammersmith Hospital, U.K.
The data showing status value of normal healthy subject comprises mean and standard deviation of status values of each point which are obtained from plural normal healthy subjects. The data showing status value of normal healthy subject are called as normal brain database. The Z-score is obtained by dividing difference between the status value of patient and the status value of normal healthy subject at each point by standard deviation at each point of normal brain database. See equation (1).Z(x,y,z)=(Imean(x,y,z)−I(x,y,z))/SD(x,y,z)  (1)Where Z(x,y,z) is Z-score at the point of coordinate x,y,z, Imean(x,y,z) is mean value of status values (voxel values associated to functional status measured by PET of SPECT etc.) at said point of normal healthy subjects, I(x,y,z) is status value at said point of the patient and SD(x,y,z) is standard deviation of status values at the point of normal healthy subjects. Imean(x,y,z) and SD(x,y,z) can be obtained from the normal brain database.
In this method, difference between normal healthy subject and patient can be clearly shown by using the normal brain database having standard deviation.
In 3D-SSP, the biggest status value from brain surface to predetermined depth perpendicular to the brain surface is selected as representative status value and is displayed on the brain surface. Then, Z-score is calculated by comparing the selected status values of patient with that of normal healthy subjects. Images of Z-score are displayed as right-brain lateral surface RT-LAT, left-brain lateral surface LT-LAT, top surface SUP, bottom surface INF, anterior surface ANT, posterior surface POST, right-brain medial surface R-MED and left-brain medial surface L-MED as shown in FIG. 2. In FIG. 2, upper images (denoted “surface”) show status values of brain surface and lower images (denoted “GLB”) show Z-score of brain surface. Z-score image enables to improve detection ability of disease and to assess severity of disease.
Mean value and standard deviation of selected status values of brain surface (said selected biggest values) at each point of brain surface of plural normal healthy subjects should be provided as normal brain database in the 3D-SSP method.
To achieve high diagnosing ability, the normal brain database is made based on preferably at least 10 normal healthy subjects. See Chen W P et al., “Effect of sample size for normal brain database on diagnostic performance of brain FDG PET for the detection of Alzheimer's disease using automated image analysis” Nucl Med Commun. 2008 March; 29 (3):270-6.
It is not easy to gather image data of normal healthy subjects, because most of functional brain images such as PET images and SPECT images gathered by the medical center are the functional brain images of subjects who visit medical center and possibly have any disease. Further, functional brain image may vary according to radio isotopes corresponding to disease to be diagnosed and materials indicating them. Therefore, normal brain database should be generated for each combination of radio isotopes and materials indicating them. Above mentioned situations disturb the generation of normal brain database.